Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm

Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

Quantum-Inspired Particle Swarm Optimization Algorithm Encoded by Probability Amplitudes of Multi-Qubits

To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration, updating any qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

基于IQPSO-EKF的多传感器融合姿态测量方法研究

为解决自动化竖井掘进设备的定位调姿精度对竖井、孔桩挖掘效率与质量的影响,提出了一种基于改进量子粒子群(IQPSO)-扩展卡尔曼滤波(EKF)的姿态测量算法,以提高微机电系统(MEMS)传感器测量精度。首先,对MEMS传感器数据进行了预处理(除噪、滤波、校准等);然后,参考现有飞行器的坐标系,建立了姿态解算模型,通过姿态角数学模型及运动学分析,构建了EFK状态方程,针对EKF方法参数估计不准确的问题,以分段混沌映射优化初始种群,引入平均位置最优值来避免陷入局部最优的IQPSO-EFK算法,优化EKF的系统、测量噪声的协方差参数;最后,对改进算法和三组姿态误差估计进行了对比实验。研究结果表明:对比三种典型目标函数,IQPSO-EFK相较于普通粒子群算法(QPSO-EFK)具有更强的寻优能力与收敛精度;对比三组旋转速度姿态测量误差,基于IQPSO-EKF算法的姿态测量方法在测量误差时比真实测量误差减少了约86.3%,比扩展卡尔曼滤波减少了约68.7%,比普通粒子群算法减少了约28.2%,证明该算法有效地提高了MEMS传感器测量精度。

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.

基于IQPSO-GA优化ANFIS模型的服务器故障预警方法

针对服务器底层部分业务类硬件故障对系统稳定运行的影响,提出一种改进的量子行为粒子群优化(IQPSO)与遗传算法(GA)相结合的混合元启发式优化算法对自适应神经模糊推理系统(ANFIS)参数进行训练,以获得更准确的ANFIS规则进行硬件故障预警的方法;首先,通过分析服务器业务与硬件相关参数之间的映射关系,通过采集的数据集对ANFIS模型进行训练构造预测模型;其次,考虑ANFIS在梯度计算过程中存在容易陷入局部最优值的问题,设计了一种IQPSO算法结合GA中的交叉和变异算子操作混合元启发算法全局搜索ANFIS规则参数;最后,通过一组后处理样本数据集对所提方法有效性和稳定性进行了检验;实验结果表明,该方法可有效预警服务器硬件故障,基于所提混合元启发优化算法获得的ANFIS模型具备更快的收敛速度和更高的全局搜索精度,与传统ANFIS模型相比泛化精度提高了47%以上。

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

Quantum control based on three forms of Lyapunov functions

This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.Stability is analyzed by the La Salle invariance principle and the numerical simulation is carried out in a 2D test system.The calculation process for the Lyapunov function is based on a combination of the average of virtual mechanical quantities, the particle swarm algorithm and a simulated annealing algorithm.Finally, a unified form of the control laws under the three forms is given.

Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm

Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach.

A novel mapping algorithm for three-dimensional network on chip based on quantum-behaved particle swarm optimization

Mapping of three-dimensional network on chip is a key problem in the research of three-dimensional network on chip. The quality of the mapping algorithm used di- rectly affects the communication efficiency between IP cores and plays an important role in the optimization of power consumption and throughput of the whole chip. In this paper, ba- sic concepts and related work of three-dimensional network on chip are introduced. Quantum-behaved particle swarm op- timization algorithm is applied to the mapping problem of three-dimensional network on chip for the first time. Sim- ulation results show that the mapping algorithm based on quantum-behaved particle swarm algorithm has faster con- vergence speed with much better optimization performance compared with the mapping algorithm based on particle swarm algorithm. It also can effectively reduce the power consumption of mapping of three-dimensional network on chip.

An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing

Nowadays,succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers.Hence,to secure both data and keys ensuring secured data storage and access,our proposed work designs a Novel Quantum Key Distribution(QKD)relying upon a non-commutative encryption framework.It makes use of a Novel Quantum Key Distribution approach,which guarantees high level secured data transmission.Along with this,a shared secret is generated using Diffie Hellman(DH)to certify secured key generation at reduced time complexity.Moreover,a non-commutative approach is used,which effectively allows the users to store and access the encrypted data into the cloud server.Also,to prevent data loss or corruption caused by the insiders in the cloud,Optimized Genetic Algorithm(OGA)is utilized,which effectively recovers the data and retrieve it if the missed data without loss.It is then followed with the decryption process as if requested by the user.Thus our proposed framework ensures authentication and paves way for secure data access,with enhanced performance and reduced complexities experienced with the prior works.

基于改进引力搜索算法的水轮机调节系统仿真

针对现阶段水电机组存在多种复杂工况、工程计算受限于算法本身的复杂性等问题,提出一种改进的引力搜索算法(改进PSOGSA),以此提高水轮机控制参数的优化性能,弥补传统控制策略难以满足动态需求的不足.首先,结合PSO算法,在GSA的速度更新公式中引入学习因子进行改进.其次,应用一种权重系数优化其位置更新公式,提高算法的自适应性.最后,结合相关仿真建模试验,使用所提改进PSOGSA对水轮机调节系统PID参数进行优化调节.仿真结果表明,在5%空载频率扰动下,改进PSOGSA的PID控制器明显优于上述传统算法,所调节的模型系统能在更短时间内趋于稳定,此时的超调量远低于传统算法,表明此改进PSOGSA在后续迭代中具备更高的迭代效率,并且改善了常规算法中易陷入局部最优的问题,从而证明了改进PSOGSA的合理有效性,水轮机调节系统的控制效果在一定程度上得到优化.

考虑碳排放的分布式电源优化配置

对分布式电源接入配电网进行合理的优化配置,能在兼顾运营商和用户利益的同时,改善系统整体电压分布。建立了综合考虑分布式电源投资成本、用户购电成本、网损费用和碳排放费用的多目标优化模型。利用改进层次分析法确定各目标的权重,进而转化为单目标函数规划问题。针对天牛须算法个体单一性在解决高维复杂问题时精度低,优化效果不佳的问题,提出了一种改进天牛须粒子群算法,利用混沌映射对参数进行调整,引入动态惯性权重、莱维飞行机制,提高了收敛速度。以IEEE33节点系统为例,将改进天牛须粒子群算法与粒子群算法及天牛须粒子群算法的效果对比,验证改进算法对分布式电源优化配置问题的可行性,有效降低了碳排放费用、用户购电费用,减少了系统网损,改善了系统整体电压分布。

钻孔瞬变电磁法扫描探测RCQPSO-LMO组合算法2.5D反演

利用钻孔进行超前探测地质构造及含水体是地下开挖工程中的常规手段,如何利用这些钻孔进行钻孔瞬变电磁法扫描探测,从而实现钻孔孔壁外围地质异常体的精细探测,对实现地下工程地质透明化具有重要的指导意义.本文提出钻孔瞬变电磁法扫描探测2.5D反演的数据解译方法,首先针对随机性反演算法时效性低,易陷入局部最优解,而确定性反演算法依赖初始模型的问题,提出了组合策略的量子粒子群优化算法用来随机搜索最优初始模型.在此基础上,利用Levenberg-Marquarat方法求解Occam反演的目标函数,形成了RCQPSO-LMO组合算法进行2.5D反演,通过对比组合算法和单一算法,验证了组合算法具有更精确的反演结果.其次结合屏蔽条件下扫描探测,对比分析了有无屏蔽的2.5D反演结果,通过设定屏蔽系数对非探测方向信号进行部分压制,可以较好地解决钻孔径向扫描探测中对非探测方向信号部分屏蔽下的反演及成像.最后建立三组理论模型进行组合算法2.5D反演,结果表明:组合算法反演结果与理论模型的一致性较好,对低阻异常体的反演精度较高,验证了组合算法对钻孔孔壁外围低阻异常体具有较高的反演精度和分辨能力.

采用改进多目标粒子群算法的斜拉桥阻尼器参数优化

为克服大跨度斜拉桥黏滞阻尼器优化设计效率低、多个相互制约的减震控制目标的问题难以权衡,基于遗传算法的“变异”方法,提出了改进多目标粒子群算法来进行阻尼器参数优化设计。建立大跨度斜拉桥的有限元模型,开展了全桥地震响应分析,根据抗震需求在桥梁纵向设置黏滞阻尼器;分别建立了塔底弯矩、阻尼力和梁端位移的减震响应与阻尼器参数之间的响应面数学模型;以减震响应面模型为研究对象,通过该算法进行阻尼器参数全局自动寻优分析,确定了阻尼器的最优参数,并与采用参数敏感性分析方法确定的一组阻尼参数进行对比分析。研究结果表明:该优化方法具有计算精度好、优化效率高和更好地权衡多个相互制约的减震控制目标的优点;通过优化算法获得的阻尼器参数组合相比采用参数敏感性分析方法获得的阻尼参数组合的减震响应,塔底弯矩增大1.73%,阻尼力减小5.97%,梁端位移减小1.66%;在无需多次有限元试算的基础上确定了更高精度的阻尼器优化参数组合,在提高减震效果的同时大大提升了计算效率。

电动汽车双层优化模型的充放电调度策略

传统的分时电价策略虽然一定程度上可以改善电动汽车无序充电所产生的电网日负荷峰谷差加大、负荷率降低等状况,但易产生新的负荷高峰,并且当前多目标优化等策略削峰填谷效果欠佳或用户参与度不高。针对上述问题,提出一种基于双层优化模型的调度策略以充分考虑电网和用户两侧需求。第1层模型以优化电网日负荷方差最小为目标函数;第2层优化模型建立以车主充电成本最小以及保证用户出行需求的目标函数,然后用改进的粒子群-模拟退火算法对双层优化模型进行循环迭代求解,并将第2层优化后的结果反馈给第1层,以此循环优化,输出最终结果。对比优化前后的负荷曲线,结果表明:与当前优化策略相比,所提出的基于双层优化模型的V2G调度策略能有效降低新的负荷高峰及负荷峰谷差,减少参与V2G的用户成本,实现两侧双赢。